AI-Enhanced Macro-Mechanic Property Prediction Using Rock Slice Using Zero-Sample Segmentation and Numerical Analysis

This paper proposes an intelligent analysis method of rock sheet based on the segment anything model (SAM) with zero samples, which combines small sample training with deep learning to realize high-precision automatic identification and segmentation of rock minerals, and then converts the segmentation results into vectorized data by using image processing technology to construct the numerical model of rock minerals, and ultimately realizes rock sheet from image identification to physical and mechanical research. The results show that the SAM-based zero-sample segmentation method can accurately and efficiently identify different mineral components in multi-component complex rock flakes. Numerical simulation results show that the numerical model of rock minerals generated by the method can effectively reflect the microstructural characteristics of rocks and accurately predict their mechanical behaviors, and the resulting elastic modulus matches well with the existing literature data, with a relative error of only 3.4%, suggesting that the proposed method provides reasonable predictive capability for rock mechanical behavior. Compared with the traditional measurement methods, this method realizes the automation and intelligence of rock thin-section analysis and enhances the adaptability to different rock samples, providing an efficient tool means for geological exploration, petroleum engineering, and geotechnical research.